Scalable Machine Learning for Visual Data
By: Xinnan Yu
Publisher: [publisher not identified]
Published: 2015
Place of publication: [New York, N.Y.?]
Language: eng
Description: Recent years have seen a rapid growth of visual data produced by social media, large-scale surveillance cameras, biometrics sensors, and mass media content providers. The unprecedented availability of visual data calls for machine learning methods that are effective and efficient for such large-scale settings. The input of any machine learning algorithm consists of data and supervision. In a large-scale setting, on the one hand, the data often comes with a large number of samples, each with high dimensionality. On the other hand, the unconstrained visual data requires a large amount of supervision to make machine learning methods effective. However, the supervised information is often limited and expensive to acquire. The above hinder the applicability of machine learning methods for large-scale visual data. In the thesis, we propose innovative approaches to scale up machine learning to address challenges arising from both the scale of the data and the limitation of the supervision. The methods are developed with a special focus on visual data, yet they are also widely applicable to other domains that require scalable machine learning methods.

Learning with high-dimensionality: The "large-scale" of visual data comes not only from the number of samples but also from the dimensionality of the features. While a considerable amount of effort has been spent on making machine learning scalable for more samples, few approaches are addressing learning with high-dimensional data. In Part I, we propose an innovative solution for learning with very high-dimensional data. Specifically, we use a special structure, the circulant structure, to speed up linear projection, the most widely used operation in machine learning. The special structure dramatically improves the space complexity from quadratic to linear, and the computational complexity from quadratic to linearithmic in terms of the feature dimension. The proposed approach is successfully applied in various frameworks of large-scale visual data analysis, including binary embedding, deep neural networks, and kernel approximation. The significantly improved efficiency is achieved with minimal loss of the performance.

For all the applications, we further propose to optimize the projection parameters with training data to further improve the performance. The scalability of learning algorithms is often fundamentally limited by the amount of supervision available. The massive visual data comes unstructured, with diverse distribution and high-dimensionality -- it is required to have a large amount of supervised information for the learning methods to work. Unfortunately, it is difficult, and sometimes even impossible to collect a sufficient amount of high-quality supervision, such as instance-by-instance labels, or frame-by-frame annotations of the videos. Learning from label proportions: To address the challenge, we need to design algorithms utilizing new types of supervision, often presented in weak forms, such as relatedness between classes, and label statistics over the groups. In Part II, we study a learning setting called Learning from Label Proportions (LLP), where the training data is provided in groups, and only the proportion of each class in each group is known.

The task is to learn a model to predict the class labels of the individuals. Besides computer vision, this learning setting has broad applications in social science, marketing, and healthcare, where individual-level labels cannot be obtained due to privacy concerns. We provide theoretical analysis under an intuitive framework called Empirical Proportion Risk Minimization (EPRM), which learns an instance level classifier to match the given label proportions on the training data. The analysis answers the fundamental question, when and why LLP is possible. Under EPRM, we propose the proportion-SVM (∝SVM) algorithm, which jointly optimizes the latent instance labels and the classification model in a large-margin framework. The approach avoids making restrictive assumptions on the data, leading to the state-of-the-art results. We have successfully applied the developed tools to challenging problems in computer vision including instance-based event recognition, and attribute modeling. Scaling up mid-level visual attributes: Besides learning with weak supervision, the limitation on the supervision can also be alleviated by leveraging the knowledge from different, yet related tasks.

Specifically, "visual attributes" have been extensively studied in computer vision. The idea is that the attributes, which can be understood as models trained to recognize visual properties can be leveraged in recognizing novel categories (being able to recognize green and orange is helpful for recognizing apple). In a large-scale setting, the unconstrained visual data requires a high-dimensional attribute space that is sufficiently expressive for the visual world. Ironically, though designed to improve the scalability of visual recognition, conventional attribute modeling requires expensive human efforts for labeling the detailed attributes and is inadequate for designing and learning a large set of attributes. To address such challenges, in Part III, we propose methods that can be used to automatically design a large set of attribute models, without user labeling burdens. We propose weak attribute, which combines various types of existing recognition models to form an expressive space for visual recognition and retrieval.

In addition, we develop category-level attribute to characterize distinct properties separating multiple categories. The attributes are optimized to be discriminative to the visual recognition task over known categories, providing both better efficiency and higher recognition rate over novel categories with a limited number of training samples.
Notes: Department: Electrical Engineering.

Thesis advisor: Shih-Fu Chang.

Thesis (Ph.D.)--Columbia University, 2015.